



'''simple docstring'''

from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
			from transformers import (
			    TFAutoModel,
			    TFAutoModelForCausalLM,
			    TFAutoModelForMaskedLM,
			    TFAutoModelForPreTraining,
			    TFAutoModelForQuestionAnswering,
			    TFAutoModelForSeqaSeqLM,
			    TFAutoModelForSequenceClassification,
			    TFAutoModelForTableQuestionAnswering,
			    TFAutoModelForTokenClassification,
			    TFAutoModelWithLMHead,
			    TFBertForMaskedLM,
			    TFBertForPreTraining,
			    TFBertForQuestionAnswering,
			    TFBertForSequenceClassification,
			    TFBertModel,
			    TFFunnelBaseModel,
			    TFFunnelModel,
			    TFGPTaLMHeadModel,
			    TFRobertaForMaskedLM,
			    TFTaForConditionalGeneration,
			    TFTapasForQuestionAnswering,
			)
			from transformers.models.auto.modeling_tf_auto import (
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TF_MODEL_MAPPING,
			)
			from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST

class 							a__( snake_case__     ):
			a_	: Optional[Any]									=      '''new-model'''


if is_tf_available():

			class 							a__( snake_case__     ):
						a_	: Union[str, Any]									=      NewModelConfig

@require_tf
class 							a__( unittest.TestCase     ):






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     ='bert-base-cased'
					snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					snake_case__	     =TFAutoModel.from_pretrained(_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     ='bert-base-cased'
					snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					snake_case__	     =TFAutoModelForPreTraining.from_pretrained(_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelForCausalLM.from_pretrained(_UpperCAmelCase						)
							snake_case__     ,	snake_case__	     =TFAutoModelForCausalLM.from_pretrained(_UpperCAmelCase					,   output_loading_info=_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							int:
					for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelWithLMHead.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							str:
					for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelForMaskedLM.from_pretrained(_UpperCAmelCase						)
							snake_case__     ,	snake_case__	     =TFAutoModelForMaskedLM.from_pretrained(_UpperCAmelCase					,   output_loading_info=_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Tuple:
					for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						)
							snake_case__     ,	snake_case__	     =TFAutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase					,   output_loading_info=_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["bert-base-uncased"]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							str:
					# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["bert-base-uncased"]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelForQuestionAnswering.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			@slow
			@require_tensorflow_probability
			def 						_lowercase  (     self						)							->							Tuple:
					for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
							snake_case__	     =AutoConfig.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =TFAutoModelForTableQuestionAnswering.from_pretrained(_UpperCAmelCase						)
							snake_case__     ,	snake_case__	     =TFAutoModelForTableQuestionAnswering.from_pretrained(
							    _UpperCAmelCase					,   output_loading_info=_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =TFAutoModelWithLMHead.from_pretrained(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)
					self.assertEqual(model.num_parameters()					,   1_4410						)
					self.assertEqual(model.num_parameters(only_trainable=_UpperCAmelCase						)					,   1_4410						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =TFAutoModelWithLMHead.from_pretrained(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)
					self.assertEqual(model.num_parameters()					,   1_4410						)
					self.assertEqual(model.num_parameters(only_trainable=_UpperCAmelCase						)					,   1_4410						)






			def 						_lowercase  (     self						)							->							str:
					# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
					snake_case__	     =TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					snake_case__	     =copy.deepcopy(model.config						)
					snake_case__	     =['FunnelBaseModel']
					snake_case__	     =TFAutoModel.from_config(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							model.save_pretrained(_UpperCAmelCase						)
							snake_case__	     =TFAutoModel.from_pretrained(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					try:
							AutoConfig.register('new-model'					,   _UpperCAmelCase						)

							snake_case__	     =[
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelForTokenClassification,
							]

							for auto_class in auto_classes:
									with self.subTest(auto_class.__name__						):
											# Wrong config class will raise an error
											with self.assertRaises(_UpperCAmelCase						):
													auto_class.register(_UpperCAmelCase					,   _UpperCAmelCase						)
											auto_class.register(_UpperCAmelCase					,   _UpperCAmelCase						)
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(_UpperCAmelCase						):
													auto_class.register(_UpperCAmelCase					,   _UpperCAmelCase						)

											# Now that the config is registered, it can be used as any other config with the auto-API
											snake_case__	     =BertModelTester(self						).get_config()
											snake_case__	     =NewModelConfig(**tiny_config.to_dict()						)
											snake_case__	     =auto_class.from_config(_UpperCAmelCase						)
											self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

											with tempfile.TemporaryDirectory() as tmp_dir:
													model.save_pretrained(_UpperCAmelCase						)
													snake_case__	     =auto_class.from_pretrained(_UpperCAmelCase						)
													self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					finally:
							if "new-model" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["new-model"]
							for mapping in (
							    TF_MODEL_MAPPING,
							    TF_MODEL_FOR_PRETRAINING_MAPPING,
							    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
							    TF_MODEL_FOR_MASKED_LM_MAPPING,
							):
									if NewModelConfig in mapping._extra_content:
											del mapping._extra_content[NewModelConfig]






			def 						_lowercase  (     self						)							->							Optional[int]:
					with self.assertRaisesRegex(
					    _UpperCAmelCase					,   'bert-base is not a local folder and is not a valid model identifier'						):
							snake_case__	     =TFAutoModel.from_pretrained('bert-base'						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					with self.assertRaisesRegex(
					    _UpperCAmelCase					,   r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
							snake_case__	     =TFAutoModel.from_pretrained(_UpperCAmelCase					,   revision='aaaaaa'						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					with self.assertRaisesRegex(
					    _UpperCAmelCase					,   'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'					,   ):
							snake_case__	     =TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'						)






			def 						_lowercase  (     self						)							->							str:
					with self.assertRaisesRegex(_UpperCAmelCase					,   'Use `from_pt=True` to load this model'						):
							snake_case__	     =TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'						)






			def 						_lowercase  (     self						)							->							Any:
					# Make sure we have cached the model.
					snake_case__	     =TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'						)
					with RequestCounter() as counter:
							snake_case__	     =TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'						)
							self.assertEqual(counter.get_request_count					,   0						)
							self.assertEqual(counter.head_request_count					,   1						)
							self.assertEqual(counter.other_request_count					,   0						)

					# With a sharded checkpoint
					snake_case__	     =TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'						)
					with RequestCounter() as counter:
							snake_case__	     =TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'						)
							self.assertEqual(counter.get_request_count					,   0						)
							self.assertEqual(counter.head_request_count					,   1						)
							self.assertEqual(counter.other_request_count					,   0						)









'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  None


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {
    '''camembert-base''': 5_1_2,
}

SCREAMING_SNAKE_CASE__     :						Any	=  '''▁'''

class 							a__( snake_case__     ):
			a_	: Tuple									=      VOCAB_FILES_NAMES
			a_	: Optional[int]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      CamembertTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]					,   **_UpperCAmelCase					,   )							->							str:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else mask_token

					super().__init__(
					    _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   additional_special_tokens=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =vocab_file
					snake_case__	     =False if not self.vocab_file else True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:

					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__	     =[self.cls_token_id]
					snake_case__	     =[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'						)

					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
							copyfile(self.vocab_file					,   _UpperCAmelCase						)

					return (out_vocab_file,)




